Multi-omics Approaches to Uncover Liquid-Based Cancer-Predicting Biomarkers in Lynch Syndrome

Background Lynch syndrome is a genetic cancer-predisposing syndrome caused by pathogenic mutations in DNA mismatch repair (path_MMR) genes. Due to the elevated cancer risk, novel screening methods, alongside current surveillance techniques could enhance cancer risk stratification. Here we show how multi-omics integration could be utilized to pinpoint cancer-predicting biomarkers in Lynch Syndrome. We studied which blood-based circulating microRNAs and metabolites could predict Lynch Syndrome cancer occurrence within a 5.8-year prospective surveillance period. Methods The study cohort consisted of 116 Lynch Syndrome carriers who were healthy at the time of sampling, of whom 17 developed cancer during the surveillance. Principal Coordinate Analysis and Canonical Correlation Analysis were used to explore the relationships between single and multi-omics data, enabling the identification of patterns and correlations across different biological layers. Weighted Correlation Network Analysis was used to identify omics-level co-expression modules and to study how these modules are associated with future cancer incidence or path_MMR variant. Lasso Cox regression was used to identify cancer-predicting biomarkers. The initial model was internally validated by splitting the data randomly into 5 training and corresponding validation datasets. Biological functions of future cancer-associated circulating microRNAs were studied by conducting pathway analyses using miRWalk. Results Weighted Correlation Network Analysis revealed a circulating microRNA co-expression module significantly associated with future cancer incidence. The identified microRNAs regulate cancer-related pathways including PI3K/Akt signaling pathway. Also, the analysis detected a circulating metabolite module, consisting of ApoB containing lipoprotein classes, (low-, intermediate-, and very low-density lipoproteins), and included cholesterols, as well as phospholipids and sphingomyelins, that had distinct levels between the path_MMRvariants. Three biomarkers- hsa-miR-101-3p, hsa-miR-183-5p, and the among of triglycerides in high-density lipoprotein particles (HDL_TG)- significantly predicted cancer risk based on Lasso Cox regression, with a C-index of 0.76 (p-value = 0.0007), where elevated levels of these biomarkers were indicators of increased hazard ratio. In the internal validation, the model had an average C-index of 0.72. Conclusions The multi-omics approach and the identified biomarkers offer a promising tool for cancer risk identification in Lynch Syndrome while also uncovering underlying systemic molecular mechanisms.